Graph mining : practical uses and instruments for exploring complex networks / edited by Riju Bhattacharya, Yogesh Kumar Rathore, Tien Anh Tran, Suman Kumar Swarnkar.

Format:

Book

Contributor:

Bhattacharya, Riju, editor.

Rathore, Yogesh Kumar, editor.

Tran, Tien Anh, editor.

Swarnkar, Suman, editor.

Series:

Synthesis Lectures on Computer Science, 1932-1686

Language:

English

Subjects (All):

Computer science.

Image processing--Digital techniques.

Image processing.

Computer vision.

Data mining.

Pattern recognition systems.

Machine learning.

Quantitative research.

Physical Description:

1 online resource (xv, 130 pages) : illustrations (some color).

Edition:

1st ed. 2026.

Other Title:

Practical uses and instruments for exploring complex networks

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, [2026]

Summary:

This book provides a thorough introduction to graph mining and addresses foundational concepts and advanced techniques along with practical applications across various fields. As graphs have become increasingly vital for data representation in domains such as social network analysis, bioinformatics, and transportation, there is a growing demand for a comprehensive source that covers both theory and practical insights. This book seeks to fill that gap by offering clear explanations, practical examples, and actionable insights, making complex graph mining techniques accessible to students, postgraduate readers, and researchers. The authors also provide an extensive investigation into the process of gaining insightful knowledge from graph representations, ranging from interpreting intricate relationships to decoding complex data structures. Readers will learn to identify anomalous patterns, locate communities, arrange nodes, predict connections, and evaluate graphs effectively. In addition, this book: Offers a concise road map for overcoming the challenges associated with graph-based data analysis Explains complex graph mining principles and methods in a clear, accessible manner Introduces cutting-edge trends in graph mining and presents practical applications and real-world case studies.

Contents:

Graph Mining: Power Laws and Graph Queries

Frequent Subgraphs Mining

Analyzing and Predicting Links in Graph-Based Data

Node Similarity and Classification

Graph Classification

Graph Clustering

Overlapping and Non-overlapping Communities

Anomaly Detection

Graph Summarization

Knowledge Graph Processing

Role of Deep Learning in Graph Mining

Graph Convolutional Network (GCN).

Notes:

Includes bibliographical references.

Description based on publisher supplied metadata and other sources.

ISBN:

3-031-93802-X

OCLC:

1531979181